Combining Forecasts Based on Time Series Models in Machine Learning Tasks

Abstract

The article investigates the solution of the forecasting problem using the combination of basic forecasting models for machine learning tasks. Methods of combining forecasts have been studied. Simple mean, weighted averaging, and regression combining methods were considered. The conditions and features of using each method to improve forecast accuracy are defined. A methodology for building combined forecasts based on methods of combining forecast estimates has been developed. The methodology consists of the following stages: analysis and preliminary processing of the data set; division of prepared data into training and test samples; modeling and forecasting based on basic models; formation of weight coefficients of combined forecasts based on evaluations of the effectiveness of basic models; unit for combining and evaluating forecasts. The architecture of the forecasting information system based on time series models has been developed. The efficiency of building combined forecasts for solving machine learning tasks has been studied. Methods of combining forecasts were studied on data sets that characterize changes in the dynamics of share prices of three companies.